Jour 4
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LLMs are impressive. They can extend human cognition in various ways and can be turned into a suite of virtual assistants. Yet, they have the same basic limitations as other deep learning-based systems. Generalizing accurately outside training distributions remains a problem, as their stubborn propensity to confabulate shows. Although LLMs do not take us significantly closer to AGI and, as a consequence, do not by themselves pose an existential risk to humankind, they do raise serious ethical...
Nicholas Humphrey will explore the concept of sentience as a crucial evolutionary development, discussing its role in human consciousness and social interactions. Sentience represents not just a biological but a complex psychological invention, crucial for personal identity and social fabric. He will also address Daniel Dennett’s Question “Will AI Achieve Consciousness? Wrong Question.” ReferencesHumphrey, N., & Dennett, D. C. ...
How do humans make meaning from language? And how can we build machines that think in more human-like ways? "Rational Meaning Construction" combines neural language models with probabilistic models for rational inference. Linguistic meaning is a context-sensitive mapping from natural language into a probabilistic language of thought (PLoT), a general-purpose symbolic substrate for generative world modelling. Thinking can be modelled with probabilistic programs, an expressive representation fo...
Melanie Mitchell will survey a debate in the artificial intelligence (AI) research community on the extent to which current AI systems can be said to "understand" language and the physical and social situations language encodes. She will describe arguments that have been made for and against such understanding, hypothesize about what humanlike understanding entails, and discuss what methods can be used to fairly evaluate understanding and intelligence in AI systems.&nb...